dpnp.linalg.tensorinv

dpnp.linalg.tensorinv(a, ind=2)

Compute the 'inverse' of an N-dimensional array.

For full documentation refer to numpy.linalg.tensorinv.

Parameters:

• a ({dpnp.ndarray, usm_ndarray}) -- Tensor to invert. Its shape must be 'square', i. e., prod(a.shape[:ind]) == prod(a.shape[ind:]).

• ind (int, optional) --

  Number of first indices that are involved in the inverse sum. Must be a positive integer.

  Default: 2.

Returns:

out -- The inverse of a tensor whose shape is equivalent to a.shape[ind:] + a.shape[:ind].

Return type:

dpnp.ndarray

See also

dpnp.linalg.tensordot

Compute tensor dot product along specified axes.

dpnp.linalg.tensorsolve

Solve the tensor equation a x = b for x.

Examples

>>> import dpnp as np
>>> a = np.eye(4*6).reshape((4, 6, 8, 3))
>>> ainv = np.linalg.tensorinv(a, ind=2)
>>> ainv.shape
(8, 3, 4, 6)
>>> b = np.random.normal(size=(4, 6))
>>> np.allclose(np.tensordot(ainv, b), np.linalg.tensorsolve(a, b))
array(True)

>>> a = np.eye(4*6).reshape((24, 8, 3))
>>> ainv = np.linalg.tensorinv(a, ind=1)
>>> ainv.shape
(8, 3, 24)
>>> b = np.random.normal(size=24)
>>> np.allclose(np.tensordot(ainv, b, axes=1), np.linalg.tensorsolve(a, b))
array(True)